diff --git a/DESCRIPTION b/DESCRIPTION
index ef188b8..37f1ed4 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,7 +1,7 @@
 Package: pharmr
 Encoding: UTF-8
-Version: 1.4.0
-Date: 2024-12-04
+Version: 1.5.0
+Date: 2025-01-15
 Title: Interface to the 'Pharmpy' 'Pharmacometrics' Library
 Authors@R: c(
   person("Rikard", "Nordgren", role=c("aut", "cre", "cph"), email="rikard.nordgren@uu.se"),
diff --git a/NAMESPACE b/NAMESPACE
index 4283a1a..464bab2 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -59,6 +59,7 @@ export(calculate_ucp_scale)
 export(check_dataset)
 export(check_high_correlations)
 export(check_parameters_near_bounds)
+export(check_setup)
 export(cleanup_model)
 export(convert_model)
 export(create_basic_pk_model)
diff --git a/R/functions_wrapper.R b/R/functions_wrapper.R
index 02b7fea..35b06e7 100644
--- a/R/functions_wrapper.R
+++ b/R/functions_wrapper.R
@@ -489,6 +489,8 @@ add_indirect_effect <- function(model, expr, prod=TRUE) {
 #' 
 #' @param model (Model) Pharmpy model
 #' @param name (str) Name of individual/pk parameter
+#' @param init (numeric) Initial estimate of the population parameter
+#' @param lower (numeric) Lower bound for the population parameter
 #'  
 #' @return (Model) Pharmpy model object
 #' 
@@ -500,8 +502,8 @@ add_indirect_effect <- function(model, expr, prod=TRUE) {
 #' }
 #' 
 #' @export
-add_individual_parameter <- function(model, name) {
-	func_out <- pharmpy$modeling$add_individual_parameter(model, name)
+add_individual_parameter <- function(model, name, init=0.1, lower=0.0) {
+	func_out <- pharmpy$modeling$add_individual_parameter(model, name, init=init, lower=lower)
 	return(py_to_r(func_out))
 }
 
@@ -709,7 +711,7 @@ add_pd_iiv <- function(model, initial_estimate=0.09) {
 #' ==  ===================================================
 #' 
 #' @param model (Model) Pharmpy model
-#' @param name (str) Name of compartment to add peripheral to.
+#' @param name (str (optional)) Name of compartment to add peripheral to.
 #'  
 #' @return (Model) Pharmpy model object
 #' 
@@ -1353,11 +1355,11 @@ calculate_eta_shrinkage <- function(model, parameter_estimates, individual_estim
 #' 
 #' @param model (Model) A previously estimated model
 #' @param expr_or_exprs (array(BooleanExpr) or array(Expr) or array(str) or BooleanExpr or Expr or str) Parameter estimates
-#' @param parameter_estimates (array) Parameter uncertainty covariance matrix
+#' @param parameter_estimates () Parameter uncertainty covariance matrix
 #' @param covariance_matrix (data.frame (optional)) expression or iterable of str or expressions
 #' Expressions or equations for parameters of interest. If equations are used
 #' the names of the left hand sides will be used as the names of the parameters.
-#' @param seed (numeric (optional)) Random number generator or integer seed
+#' @param seed (numeric) Random number generator or integer seed
 #'  
 #' @return (data.frame) A DataFrame of statistics indexed on parameter and covariate value.
 #' 
@@ -1372,8 +1374,8 @@ calculate_eta_shrinkage <- function(model, parameter_estimates, individual_estim
 #' }
 #' 
 #' @export
-calculate_individual_parameter_statistics <- function(model, expr_or_exprs, parameter_estimates, covariance_matrix=NULL, seed=NULL) {
-	parameter_estimates <- convert_input(parameter_estimates, "pd.Series")
+calculate_individual_parameter_statistics <- function(model, expr_or_exprs, parameter_estimates, covariance_matrix=NULL, seed=1234) {
+	parameter_estimates <- convert_input(parameter_estimates, "Mapping")
 	seed <- convert_input(seed, "int")
 	func_out <- pharmpy$modeling$calculate_individual_parameter_statistics(model, expr_or_exprs, parameter_estimates, covariance_matrix=covariance_matrix, seed=seed)
 	func_out <- reset_index_df(func_out)
@@ -1457,7 +1459,7 @@ calculate_parameters_from_ucp <- function(model, scale, ucps) {
 #' @param model (Model) A previously estimated model
 #' @param parameter_estimates (array) Parameter estimates
 #' @param covariance_matrix (data.frame (optional)) Parameter uncertainty covariance matrix
-#' @param seed (numeric (optional)) Random number generator or seed
+#' @param seed (numeric) Random number generator or seed
 #'  
 #' @return (data.frame) A DataFrame of statistics indexed on parameter and covariate value.
 #' 
@@ -1475,7 +1477,7 @@ calculate_parameters_from_ucp <- function(model, scale, ucps) {
 #' 
 #' 
 #' @export
-calculate_pk_parameters_statistics <- function(model, parameter_estimates, covariance_matrix=NULL, seed=NULL) {
+calculate_pk_parameters_statistics <- function(model, parameter_estimates, covariance_matrix=NULL, seed=1234) {
 	parameter_estimates <- convert_input(parameter_estimates, "pd.Series")
 	seed <- convert_input(seed, "int")
 	func_out <- pharmpy$modeling$calculate_pk_parameters_statistics(model, parameter_estimates, covariance_matrix=covariance_matrix, seed=seed)
@@ -1902,7 +1904,7 @@ create_joint_distribution <- function(model, rvs=NULL, individual_estimates=NULL
 #' Pharmpy functions that use random sampling take a random number generator or seed as input.
 #' This function can be used to create a default new random number generator.
 #' 
-#' @param seed (numeric (optional)) Seed for the random number generator or NULL (default) for a randomized seed. If seed
+#' @param seed (numeric) Seed for the random number generator or NULL (default) for a randomized seed. If seed
 #' is generator it will be passed through.
 #'  
 #' @return (Generator) Initialized numpy random number generator object
@@ -1914,7 +1916,7 @@ create_joint_distribution <- function(model, rvs=NULL, individual_estimates=NULL
 #' }
 #' 
 #' @export
-create_rng <- function(seed=NULL) {
+create_rng <- function(seed=1234) {
 	seed <- convert_input(seed, "int")
 	func_out <- pharmpy$modeling$create_rng(seed=seed)
 	return(py_to_r(func_out))
@@ -3482,7 +3484,7 @@ get_unit_of <- function(model, variable) {
 #' 
 #' @param model (Model) Pharmpy model
 #'  
-#' @return (sympy.Matrix) Vector of inputs
+#' @return (Matrix) Vector of inputs
 #' 
 #' @examples
 #' \dontrun{
@@ -3531,7 +3533,7 @@ greekify_model <- function(model, named_subscripts=FALSE) {
 #' first (in case of many) dependent variable is going to be checked.
 #' 
 #' @param model (Model) The model to check
-#' @param dv (str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
+#' @param dv (Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
 #'  
 #' @return (logical) TRUE if the model has an additive error model and FALSE otherwise
 #' 
@@ -3565,7 +3567,7 @@ has_additive_error_model <- function(model, dv=NULL) {
 #' first (in case of many) dependent variable is going to be checked.
 #' 
 #' @param model (Model) The model to check
-#' @param dv (str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
+#' @param dv (Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
 #'  
 #' @return (logical) TRUE if the model has a combined error model and FALSE otherwise
 #' 
@@ -3874,7 +3876,7 @@ has_presystemic_metabolite <- function(model) {
 #' first (in case of many) dependent variable is going to be checked.
 #' 
 #' @param model (Model) The model to check
-#' @param dv (str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
+#' @param dv (Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
 #'  
 #' @return (logical) TRUE if the model has a proportional error model and FALSE otherwise
 #' 
@@ -4937,7 +4939,7 @@ remove_parameter_uncertainty_step <- function(model) {
 #' ==  ===================================================
 #' 
 #' @param model (Model) Pharmpy model
-#' @param name (str) Name of compartment to remove peripheral compartment from.
+#' @param name (str (optional)) Name of compartment to remove peripheral compartment from.
 #'  
 #' @return (Model) Pharmpy model object
 #' 
@@ -5174,7 +5176,7 @@ resample_data <- function(dataset_or_model, group, resamples=1, stratify=NULL, s
 #' @param individual_estimates_covariance (data.frame) Uncertainty covariance of the individual estimates
 #' @param parameters (array(str) (optional)) A vector of a subset of individual parameters to sample. Default is NULL, which means all.
 #' @param samples_per_id (numeric) Number of samples per individual
-#' @param seed (numeric (optional)) Random number generator or seed
+#' @param seed (numeric) Random number generator or seed
 #'  
 #' @return (data.frame) Pool of samples in a DataFrame
 #' 
@@ -5196,7 +5198,7 @@ resample_data <- function(dataset_or_model, group, resamples=1, stratify=NULL, s
 #' 
 #' 
 #' @export
-sample_individual_estimates <- function(model, individual_estimates, individual_estimates_covariance, parameters=NULL, samples_per_id=100, seed=NULL) {
+sample_individual_estimates <- function(model, individual_estimates, individual_estimates_covariance, parameters=NULL, samples_per_id=100, seed=1234) {
 	parameters <- convert_input(parameters, "list")
 	samples_per_id <- convert_input(samples_per_id, "int")
 	seed <- convert_input(seed, "int")
@@ -5220,7 +5222,7 @@ sample_individual_estimates <- function(model, individual_estimates, individual_
 #' default and means never and 0 means always
 #' @param force_posdef_covmatrix (logical) Set to TRUE to force the input covariance matrix to be positive definite
 #' @param n (numeric) Number of samples
-#' @param seed (numeric (optional)) Random number generator
+#' @param seed (numeric) Random number generator
 #'  
 #' @return (data.frame) A dataframe with one sample per row
 #' 
@@ -5240,7 +5242,7 @@ sample_individual_estimates <- function(model, individual_estimates, individual_
 #' 
 #' 
 #' @export
-sample_parameters_from_covariance_matrix <- function(model, parameter_estimates, covariance_matrix, force_posdef_samples=NULL, force_posdef_covmatrix=FALSE, n=1, seed=NULL) {
+sample_parameters_from_covariance_matrix <- function(model, parameter_estimates, covariance_matrix, force_posdef_samples=NULL, force_posdef_covmatrix=FALSE, n=1, seed=1234) {
 	parameter_estimates <- convert_input(parameter_estimates, "pd.Series")
 	force_posdef_samples <- convert_input(force_posdef_samples, "int")
 	n <- convert_input(n, "int")
@@ -5265,7 +5267,7 @@ sample_parameters_from_covariance_matrix <- function(model, parameter_estimates,
 #' @param force_posdef_samples (numeric (optional)) Number of samples to reject before forcing variability parameters to give
 #' positive definite covariance matrices.
 #' @param n (numeric) Number of samples
-#' @param seed (numeric (optional)) Random number generator or seed
+#' @param seed (numeric) Random number generator or seed
 #' @param scale (str) Scale to perform sampling on. Valid options are 'normal' and 'UCP'
 #'  
 #' @return (data.frame) samples
@@ -5287,7 +5289,7 @@ sample_parameters_from_covariance_matrix <- function(model, parameter_estimates,
 #' 
 #' 
 #' @export
-sample_parameters_uniformly <- function(model, parameter_estimates, fraction=0.1, force_posdef_samples=NULL, n=1, seed=NULL, scale='normal') {
+sample_parameters_uniformly <- function(model, parameter_estimates, fraction=0.1, force_posdef_samples=NULL, n=1, seed=1234, scale='normal') {
 	parameter_estimates <- convert_input(parameter_estimates, "pd.Series")
 	force_posdef_samples <- convert_input(force_posdef_samples, "int")
 	n <- convert_input(n, "int")
@@ -5315,7 +5317,7 @@ sample_parameters_uniformly <- function(model, parameter_estimates, fraction=0.1
 #' +------------------------+----------------------------------------+
 #' 
 #' @param model (Model) Set error model for this model
-#' @param dv (str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
+#' @param dv (Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
 #' @param data_trans (numeric or str or Expr (optional)) A data transformation expression or NULL (default) to use the transformation
 #' specified by the model. Series expansion will be used for approximation.
 #' @param series_terms (numeric) Number of terms to use for the series expansion approximation for data
@@ -5396,7 +5398,7 @@ set_baseline_effect <- function(model, expr='const') {
 #' +------------------------+-----------------------------------------------------+
 #' 
 #' @param model (Model) Set error model for this model
-#' @param dv (str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
+#' @param dv (Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
 #' @param data_trans (numeric or str or Expr (optional)) A data transformation expression or NULL (default) to use the transformation
 #' specified by the model.
 #'  
@@ -6069,7 +6071,7 @@ set_ode_solver <- function(model, solver) {
 #' 
 #' @param model (Model) Pharmpy model
 #' @param n (numeric) Number of transit compartments
-#' @param name (str) Name of compartment to add peripheral to.
+#' @param name (str (optional)) Name of compartment to add peripheral to.
 #'  
 #' @return (Model) Pharmpy model object
 #' 
@@ -6151,7 +6153,7 @@ set_power_on_ruv <- function(model, list_of_eps=NULL, dv=NULL, lower_limit=0.01,
 #' +------------------------+----------------------------------------+
 #' 
 #' @param model (Model) Set error model for this model
-#' @param dv (str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
+#' @param dv (Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
 #' @param data_trans (numeric or str or Expr (optional)) A data transformation expression or NULL (default) to use the transformation
 #' specified by the model.
 #' @param zero_protection (logical) Set to TRUE to add code protecting from IPRED=0
@@ -6267,7 +6269,7 @@ set_seq_zo_fo_absorption <- function(model) {
 #' }
 #' 
 #' @export
-set_simulation <- function(model, n=1, seed=64206) {
+set_simulation <- function(model, n=1, seed=1234) {
 	n <- convert_input(n, "int")
 	seed <- convert_input(seed, "int")
 	func_out <- pharmpy$modeling$set_simulation(model, n=n, seed=seed)
@@ -6283,7 +6285,7 @@ set_simulation <- function(model, n=1, seed=64206) {
 #' @param model (Model) Pharmpy model
 #' @param cutoff (numeric) A cutoff value for idv column
 #' @param idv (str) Time or time after dose, default is Time
-#' @param dv (str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
+#' @param dv (Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)
 #'  
 #' @return (Model) Pharmpy model object
 #' 
@@ -8028,8 +8030,8 @@ retrieve_models <- function(source, names=NULL) {
 #' @description
 #' Run allometry tool. For more details, see :ref:`allometry`.
 #' 
-#' @param model (Model (optional)) Pharmpy model
-#' @param results (ModelfitResults (optional)) Results for model
+#' @param model (Model) Pharmpy model
+#' @param results (ModelfitResults) Results for model
 #' @param allometric_variable (str or Expr) Name of the variable to use for allometric scaling (default is WT)
 #' @param reference_value (str or numeric or Expr) Reference value for the allometric variable (default is 70)
 #' @param parameters (array(str or Expr) (optional)) Parameters to apply scaling to (default is all CL, Q and V parameters)
@@ -8049,14 +8051,14 @@ retrieve_models <- function(source, names=NULL) {
 #' }
 #' 
 #' @export
-run_allometry <- function(model=NULL, results=NULL, allometric_variable='WT', reference_value=70, parameters=NULL, initials=NULL, lower_bounds=NULL, upper_bounds=NULL, fixed=TRUE, ...) {
+run_allometry <- function(model, results, allometric_variable='WT', reference_value=70, parameters=NULL, initials=NULL, lower_bounds=NULL, upper_bounds=NULL, fixed=TRUE, ...) {
 	tryCatch(
 	{
 		parameters <- convert_input(parameters, "list")
 		initials <- convert_input(initials, "list")
 		lower_bounds <- convert_input(lower_bounds, "list")
 		upper_bounds <- convert_input(upper_bounds, "list")
-		func_out <- pharmpy$tools$run_allometry(model=model, results=results, allometric_variable=allometric_variable, reference_value=reference_value, parameters=parameters, initials=initials, lower_bounds=lower_bounds, upper_bounds=upper_bounds, fixed=fixed, ...)
+		func_out <- pharmpy$tools$run_allometry(model, results, allometric_variable=allometric_variable, reference_value=reference_value, parameters=parameters, initials=initials, lower_bounds=lower_bounds, upper_bounds=upper_bounds, fixed=fixed, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
@@ -8103,7 +8105,7 @@ run_allometry <- function(model=NULL, results=NULL, allometric_variable='WT', re
 #' @description
 #' Run Automatic Model Development (AMD) tool
 #' 
-#' @param input (Model or str or data.frame) Starting model or dataset
+#' @param input (Model or str) Starting model or dataset
 #' @param results (ModelfitResults (optional)) Reults of input if input is a model
 #' @param modeltype (str) Type of model to build. Valid strings are 'basic_pk', 'pkpd', 'drug_metabolite' and 'tmdd'
 #' @param administration (str) Route of administration. Either 'iv', 'oral' or 'ivoral'
@@ -8123,14 +8125,14 @@ run_allometry <- function(model=NULL, results=NULL, allometric_variable='WT', re
 #' @param occasion (str (optional)) Name of occasion column
 #' @param path (str (optional)) Path to run AMD in
 #' @param resume (logical) Whether to allow resuming previous run
-#' @param strictness (str (optional)) Strictness criteria
+#' @param strictness (str) Strictness criteria
 #' @param dv_types (list(str=numeric) (optional)) Dictionary of DV types for TMDD models with multiple DVs.
 #' @param mechanistic_covariates (array(str or list(str)) (optional)) List of covariates or tuple of covariate and parameter combination to run in a
 #' separate proioritized covsearch run. For instance c("WT", ("CRCL", "CL")).
 #' The effects are extracted from the search space for covsearch.
 #' @param retries_strategy (str) Whether or not to run retries tool. Valid options are 'skip', 'all_final' or 'final'.
 #' Default is 'final'.
-#' @param seed (numeric (optional)) Random number generator or seed to be used.
+#' @param seed (numeric) Random seed to be used.
 #' @param parameter_uncertainty_method (str (optional)) Parameter uncertainty method.
 #' @param ignore_datainfo_fallback (logical) Ignore using datainfo to get information not given by the user. Default is FALSE
 #' @param .E (list(str=numeric or str) (optional)) EXPERIMENTAL FEATURE. Dictionary of different E-values used in mBIC.
@@ -8150,7 +8152,7 @@ run_allometry <- function(model=NULL, results=NULL, allometric_variable='WT', re
 #' 
 #' 
 #' @export
-run_amd <- function(input, results=NULL, modeltype='basic_pk', administration='oral', strategy='default', cl_init=NULL, vc_init=NULL, mat_init=NULL, b_init=NULL, emax_init=NULL, ec50_init=NULL, met_init=NULL, search_space=NULL, lloq_method=NULL, lloq_limit=NULL, allometric_variable=NULL, occasion=NULL, path=NULL, resume=FALSE, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', dv_types=NULL, mechanistic_covariates=NULL, retries_strategy='all_final', seed=NULL, parameter_uncertainty_method=NULL, ignore_datainfo_fallback=FALSE, .E=NULL) {
+run_amd <- function(input, results=NULL, modeltype='basic_pk', administration='oral', strategy='default', cl_init=NULL, vc_init=NULL, mat_init=NULL, b_init=NULL, emax_init=NULL, ec50_init=NULL, met_init=NULL, search_space=NULL, lloq_method=NULL, lloq_limit=NULL, allometric_variable=NULL, occasion=NULL, path=NULL, resume=FALSE, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', dv_types=NULL, mechanistic_covariates=NULL, retries_strategy='all_final', seed=1234, parameter_uncertainty_method=NULL, ignore_datainfo_fallback=FALSE, .E=NULL) {
 	tryCatch(
 	{
 		mechanistic_covariates <- convert_input(mechanistic_covariates, "list")
@@ -8268,31 +8270,31 @@ run_bootstrap <- function(model, results=NULL, resamples=1, ...) {
 #' @description
 #' Run COVsearch tool. For more details, see :ref:`covsearch`.
 #' 
+#' @param model (Model) Pharmpy model
+#' @param results (ModelfitResults) Results of model
 #' @param search_space (str or ModelFeatures) MFL of covariate effects to try
 #' @param p_forward (numeric) The p-value to use in the likelihood ratio test for forward steps
 #' @param p_backward (numeric) The p-value to use in the likelihood ratio test for backward steps
 #' @param max_steps (numeric) The maximum number of search steps to make
 #' @param algorithm (str) The search algorithm to use. Currently, 'scm-forward' and
 #' 'scm-forward-then-backward' are supported.
-#' @param results (ModelfitResults (optional)) Results of model
-#' @param model (Model (optional)) Pharmpy model
 #' @param max_eval (logical) Limit the number of function evaluations to 3.1 times that of the
 #' base model. Default is FALSE.
 #' @param adaptive_scope_reduction (logical) Stash all non-significant parameter-covariate effects to be tested
 #' after all significant effects have been tested. Once all these have been
 #' tested, try adding the stashed effects once more with a regular forward approach.
 #' Default is FALSE
-#' @param strictness (str (optional)) Strictness criteria
+#' @param strictness (str) Strictness criteria
 #' @param naming_index_offset (numeric (optional)) index offset for naming of runs. Default is 0.
 #' @param nsamples (numeric) Number of samples from individual parameter conditional distribution for linear covariate model selection.
-#' `nsamples=0` uses ETAs to for linear model selection, whereas `nsample`>=1 generates MCMC samples with an
-#' additional SAEM estimation step. When multiple samples are generated, linear mixed effects model will be
-#' used to fit the linear models.
 #' Default is 10, i.e. generating 10 samples per subject
-#' @param .statsmodels (logical) Estimation tool for SAMBA linear covariate model fitting. 'TRUE' calls statsmodel's
-#' functionalities, whereas 'FALSE' calls nonmem.
-#' @param .weighted_linreg (logical) When using nonmem to run linear covariate models, 'TRUE' uses ETC as weight to run WLS.
-#' @param .lin_filter (numeric) Option to control the number of covariates passed to nonlinear selection
+#' @param .samba_max_covariates (numeric (optional)) Maximum number of covariate inclusion allowed in linear covariate screening for each parameter.
+#' @param .samba_selection_criterion (str) Method used to fit linear covariate models. Currently, Ordinary Least Squares (ols),
+#' Weighted Least Squares (wls), and Linear Mixed-Effects (lme) are supported.
+#' @param .samba_linreg_method (str) Method used for linear and nonlinear model selection in SAMBA methods. Currently, BIC and LRT are
+#' supported.
+#' @param .samba_stepwise_lcs (logical (optional)) Use stepwise linear covariate screening or not. By default, SAMBA methods use stepwise LCS whereas SCM-LCS uses
+#' non-stepwise LCS
 #' @param ... Arguments to pass to tool
 #'  
 #' @return (COVSearchResults) COVsearch tool result object
@@ -8302,18 +8304,18 @@ run_bootstrap <- function(model, results=NULL, resamples=1, ...) {
 #' model <- load_example_model("pheno")
 #' results <- load_example_modelfit_results("pheno")
 #' search_space <- 'COVARIATE(c(CL, V), c(AGE, WT), EXP)'
-#' res <- run_covsearch(search_space, model=model, results=results)
+#' res <- run_covsearch(model=model, results=results, search_space=search_space)
 #' }
 #' 
 #' @export
-run_covsearch <- function(search_space, p_forward=0.01, p_backward=0.001, max_steps=-1, algorithm='scm-forward-then-backward', results=NULL, model=NULL, max_eval=FALSE, adaptive_scope_reduction=FALSE, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', naming_index_offset=0, nsamples=10, .statsmodels=FALSE, .weighted_linreg=FALSE, .lin_filter=0, ...) {
+run_covsearch <- function(model, results, search_space, p_forward=0.01, p_backward=0.001, max_steps=-1, algorithm='scm-forward-then-backward', max_eval=FALSE, adaptive_scope_reduction=FALSE, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', naming_index_offset=0, nsamples=10, .samba_max_covariates=3, .samba_selection_criterion='bic', .samba_linreg_method='ols', .samba_stepwise_lcs=NULL, ...) {
 	tryCatch(
 	{
 		max_steps <- convert_input(max_steps, "int")
 		naming_index_offset <- convert_input(naming_index_offset, "int")
 		nsamples <- convert_input(nsamples, "int")
-		.lin_filter <- convert_input(.lin_filter, "int")
-		func_out <- pharmpy$tools$run_covsearch(search_space, p_forward=p_forward, p_backward=p_backward, max_steps=max_steps, algorithm=algorithm, results=results, model=model, max_eval=max_eval, adaptive_scope_reduction=adaptive_scope_reduction, strictness=strictness, naming_index_offset=naming_index_offset, nsamples=nsamples, `_statsmodels`=.statsmodels, `_weighted_linreg`=.weighted_linreg, `_lin_filter`=.lin_filter, ...)
+		.samba_max_covariates <- convert_input(.samba_max_covariates, "int")
+		func_out <- pharmpy$tools$run_covsearch(model, results, search_space, p_forward=p_forward, p_backward=p_backward, max_steps=max_steps, algorithm=algorithm, max_eval=max_eval, adaptive_scope_reduction=adaptive_scope_reduction, strictness=strictness, naming_index_offset=naming_index_offset, nsamples=nsamples, `_samba_max_covariates`=.samba_max_covariates, `_samba_selection_criterion`=.samba_selection_criterion, `_samba_linreg_method`=.samba_linreg_method, `_samba_stepwise_lcs`=.samba_stepwise_lcs, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
@@ -8437,16 +8439,16 @@ run_estmethod <- function(algorithm, methods=NULL, solvers=NULL, parameter_uncer
 #' @description
 #' Run IIVsearch tool. For more details, see :ref:`iivsearch`.
 #' 
+#' @param model (Model) Pharmpy model
+#' @param results (ModelfitResults) Results for model
 #' @param algorithm (str) Which algorithm to run when determining number of IIVs.
 #' @param iiv_strategy (str) If/how IIV should be added to start model. Default is 'no_add'.
 #' @param rank_type (str) Which ranking type should be used. Default is BIC.
 #' @param linearize (logical) Wheter or not use linearization when running the tool.
 #' @param cutoff (numeric (optional)) Cutoff for which value of the ranking function that is considered significant. Default
 #' is NULL (all models will be ranked)
-#' @param results (ModelfitResults (optional)) Results for model
-#' @param model (Model (optional)) Pharmpy model
 #' @param keep (array(str) (optional)) List of IIVs to keep. Default is "CL"
-#' @param strictness (str (optional)) Strictness criteria
+#' @param strictness (str) Strictness criteria
 #' @param correlation_algorithm (str (optional)) Which algorithm to run for the determining block structure of added IIVs. If NULL, the
 #' algorithm is determined based on the 'algorithm' argument
 #' @param E_p (numeric or str (optional)) Expected number of predictors for diagonal elements (used for mBIC). Must be set when using mBIC and
@@ -8461,15 +8463,15 @@ run_estmethod <- function(algorithm, methods=NULL, solvers=NULL, parameter_uncer
 #' \dontrun{
 #' model <- load_example_model("pheno")
 #' results <- load_example_modelfit_results("pheno")
-#' run_iivsearch('td_brute_force', results=results, model=model)
+#' run_iivsearch(model=model, results=results, algorithm='td_brute_force')
 #' }
 #' 
 #' @export
-run_iivsearch <- function(algorithm='top_down_exhaustive', iiv_strategy='no_add', rank_type='bic', linearize=FALSE, cutoff=NULL, results=NULL, model=NULL, keep=c('CL'), strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', correlation_algorithm=NULL, E_p=NULL, E_q=NULL, ...) {
+run_iivsearch <- function(model, results, algorithm='top_down_exhaustive', iiv_strategy='no_add', rank_type='bic', linearize=FALSE, cutoff=NULL, keep=c('CL'), strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', correlation_algorithm=NULL, E_p=NULL, E_q=NULL, ...) {
 	tryCatch(
 	{
 		keep <- convert_input(keep, "list")
-		func_out <- pharmpy$tools$run_iivsearch(algorithm=algorithm, iiv_strategy=iiv_strategy, rank_type=rank_type, linearize=linearize, cutoff=cutoff, results=results, model=model, keep=keep, strictness=strictness, correlation_algorithm=correlation_algorithm, E_p=E_p, E_q=E_q, ...)
+		func_out <- pharmpy$tools$run_iivsearch(model, results, algorithm=algorithm, iiv_strategy=iiv_strategy, rank_type=rank_type, linearize=linearize, cutoff=cutoff, keep=keep, strictness=strictness, correlation_algorithm=correlation_algorithm, E_p=E_p, E_q=E_q, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
@@ -8516,14 +8518,14 @@ run_iivsearch <- function(algorithm='top_down_exhaustive', iiv_strategy='no_add'
 #' @description
 #' Run IOVsearch tool. For more details, see :ref:`iovsearch`.
 #' 
+#' @param model (Model) Pharmpy model
+#' @param results (ModelfitResults) Results for model
 #' @param column (str) Name of column in dataset to use as occasion column (default is 'OCC')
 #' @param list_of_parameters (array(str or array(str)) (optional)) List of parameters to test IOV on, if none all parameters with IIV will be tested (default)
 #' @param rank_type (str) Which ranking type should be used. Default is BIC.
 #' @param cutoff (numeric (optional)) Cutoff for which value of the ranking type that is considered significant. Default
 #' is NULL (all models will be ranked)
 #' @param distribution (str) Which distribution added IOVs should have (default is same-as-iiv)
-#' @param results (ModelfitResults (optional)) Results for model
-#' @param model (Model (optional)) Pharmpy model
 #' @param strictness (str (optional)) Strictness criteria
 #' @param E (numeric or str (optional)) Expected number of predictors (used for mBIC). Must be set when using mBI
 #' @param ... Arguments to pass to tool
@@ -8534,15 +8536,15 @@ run_iivsearch <- function(algorithm='top_down_exhaustive', iiv_strategy='no_add'
 #' \dontrun{
 #' model <- load_example_model("pheno")
 #' results <- load_example_modelfit_results("pheno")
-#' run_iovsearch('OCC', results=results, model=model)
+#' run_iovsearch(model=model, results=results, column='OCC')
 #' }
 #' 
 #' @export
-run_iovsearch <- function(column='OCC', list_of_parameters=NULL, rank_type='bic', cutoff=NULL, distribution='same-as-iiv', results=NULL, model=NULL, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', E=NULL, ...) {
+run_iovsearch <- function(model, results, column='OCC', list_of_parameters=NULL, rank_type='bic', cutoff=NULL, distribution='same-as-iiv', strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', E=NULL, ...) {
 	tryCatch(
 	{
 		list_of_parameters <- convert_input(list_of_parameters, "list")
-		func_out <- pharmpy$tools$run_iovsearch(column=column, list_of_parameters=list_of_parameters, rank_type=rank_type, cutoff=cutoff, distribution=distribution, results=results, model=model, strictness=strictness, E=E, ...)
+		func_out <- pharmpy$tools$run_iovsearch(model, results, column=column, list_of_parameters=list_of_parameters, rank_type=rank_type, cutoff=cutoff, distribution=distribution, strictness=strictness, E=E, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
@@ -8716,15 +8718,15 @@ run_modelfit <- function(model_or_models=NULL, n=NULL, ...) {
 #' @description
 #' Run Modelsearch tool. For more details, see :ref:`modelsearch`.
 #' 
+#' @param model (Model) Pharmpy model
+#' @param results (ModelfitResults) Results for model
 #' @param search_space (str or ModelFeatures) Search space to test. Either as a string or a ModelFeatures object.
 #' @param algorithm (str) Algorithm to use.
 #' @param iiv_strategy (str) If/how IIV should be added to candidate models. Default is 'absorption_delay'.
 #' @param rank_type (str) Which ranking type should be used. Default is BIC.
 #' @param cutoff (numeric (optional)) Cutoff for which value of the ranking function that is considered significant. Default
 #' is NULL (all models will be ranked)
-#' @param results (ModelfitResults (optional)) Results for model
-#' @param model (Model (optional)) Pharmpy model
-#' @param strictness (str (optional)) Strictness criteria
+#' @param strictness (str) Strictness criteria
 #' @param E (numeric or str (optional)) Expected number of predictors (used for mBIC). Must be set when using mBI
 #' @param ... Arguments to pass to tool
 #'  
@@ -8733,15 +8735,16 @@ run_modelfit <- function(model_or_models=NULL, n=NULL, ...) {
 #' @examples
 #' \dontrun{
 #' model <- load_example_model("pheno")
-#' results <- load_example_modelfit_results("pheno")
-#' run_modelsearch('ABSORPTION(ZO);PERIPHERALS(1)', 'exhaustive', results=results, model=model)
+#' res <- load_example_modelfit_results("pheno")
+#' search_space <- 'ABSORPTION(ZO);PERIPHERALS(1)'
+#' run_modelsearch(model=model, results=res, search_space=search_space, algorithm='exhaustive')
 #' }
 #' 
 #' @export
-run_modelsearch <- function(search_space, algorithm='reduced_stepwise', iiv_strategy='absorption_delay', rank_type='bic', cutoff=NULL, results=NULL, model=NULL, strictness='minimization_successful or (rounding_errors and sigdigs >= 0.1)', E=NULL, ...) {
+run_modelsearch <- function(model, results, search_space, algorithm='reduced_stepwise', iiv_strategy='absorption_delay', rank_type='bic', cutoff=NULL, strictness='minimization_successful or (rounding_errors and sigdigs >= 0.1)', E=NULL, ...) {
 	tryCatch(
 	{
-		func_out <- pharmpy$tools$run_modelsearch(search_space, algorithm=algorithm, iiv_strategy=iiv_strategy, rank_type=rank_type, cutoff=cutoff, results=results, model=model, strictness=strictness, E=E, ...)
+		func_out <- pharmpy$tools$run_modelsearch(model, results, search_space, algorithm=algorithm, iiv_strategy=iiv_strategy, rank_type=rank_type, cutoff=cutoff, strictness=strictness, E=E, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
@@ -8793,22 +8796,20 @@ run_modelsearch <- function(search_space, algorithm='reduced_stepwise', iiv_stra
 #' @param number_of_candidates (numeric) Number of retry candidates to run. The default is 5.
 #' @param fraction (numeric) Determines allowed increase/decrease from initial parameter estimate. Default is 0.1 (10%)
 #' @param use_initial_estimates (logical) Use initial parameter estimates instead of final estimates of input model when creating candidate models.
-#' @param strictness (str (optional)) Strictness criteria. The default is "minimization_successful or (rounding_errors and sigdigs >= 0.1)".
+#' @param strictness (str) Strictness criteria. The default is "minimization_successful or (rounding_errors and sigdigs >= 0.1)".
 #' @param scale (str (optional)) Which scale to update the initial values on. Either normal scale or UCP scale.
-#' @param prefix_name (str (optional)) Prefix the candidate model names with given string.
-#' @param seed (numeric (optional)) Random number generator or seed to be used
+#' @param prefix_name (str (optional)) Prefix the candidate model names with given string
 #' @param ... Arguments to pass to tool
 #'  
 #' @return (RetriesResults) Retries tool results object.
 #' 
 #' 
 #' @export
-run_retries <- function(model=NULL, results=NULL, number_of_candidates=5, fraction=0.1, use_initial_estimates=FALSE, strictness='minimization_successful or (rounding_errors and sigdigs >= 0.1)', scale='UCP', prefix_name='', seed=NULL, ...) {
+run_retries <- function(model=NULL, results=NULL, number_of_candidates=5, fraction=0.1, use_initial_estimates=FALSE, strictness='minimization_successful or (rounding_errors and sigdigs >= 0.1)', scale='UCP', prefix_name='', ...) {
 	tryCatch(
 	{
 		number_of_candidates <- convert_input(number_of_candidates, "int")
-		seed <- convert_input(seed, "int")
-		func_out <- pharmpy$tools$run_retries(model=model, results=results, number_of_candidates=number_of_candidates, fraction=fraction, use_initial_estimates=use_initial_estimates, strictness=strictness, scale=scale, prefix_name=prefix_name, seed=seed, ...)
+		func_out <- pharmpy$tools$run_retries(model=model, results=results, number_of_candidates=number_of_candidates, fraction=fraction, use_initial_estimates=use_initial_estimates, strictness=strictness, scale=scale, prefix_name=prefix_name, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
@@ -8855,14 +8856,14 @@ run_retries <- function(model=NULL, results=NULL, number_of_candidates=5, fracti
 #' @description
 #' Run the ruvsearch tool. For more details, see :ref:`ruvsearch`.
 #' 
-#' @param model (Model (optional)) Pharmpy model
-#' @param results (ModelfitResults (optional)) Results of model
+#' @param model (Model) Pharmpy model
+#' @param results (ModelfitResults) Results of model
 #' @param groups (numeric) The number of bins to use for the time varying models
 #' @param p_value (numeric) The p-value to use for the likelihood ratio test
 #' @param skip (array(str) (optional)) A vector of models to not attempt.
 #' @param max_iter (numeric) Number of iterations to run (1, 2, or 3). For models with BLQ only one iteration is supported.
 #' @param dv (numeric (optional)) Which DV to assess the error model for.
-#' @param strictness (str (optional)) Strictness criteri
+#' @param strictness (str) Strictness criteri
 #' @param ... Arguments to pass to tool
 #'  
 #' @return (RUVSearchResults) Ruvsearch tool result object
@@ -8875,14 +8876,14 @@ run_retries <- function(model=NULL, results=NULL, number_of_candidates=5, fracti
 #' }
 #' 
 #' @export
-run_ruvsearch <- function(model=NULL, results=NULL, groups=4, p_value=0.001, skip=NULL, max_iter=3, dv=NULL, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', ...) {
+run_ruvsearch <- function(model, results, groups=4, p_value=0.001, skip=NULL, max_iter=3, dv=NULL, strictness='minimization_successful or (rounding_errors and sigdigs>=0.1)', ...) {
 	tryCatch(
 	{
 		groups <- convert_input(groups, "int")
 		skip <- convert_input(skip, "list")
 		max_iter <- convert_input(max_iter, "int")
 		dv <- convert_input(dv, "int")
-		func_out <- pharmpy$tools$run_ruvsearch(model=model, results=results, groups=groups, p_value=p_value, skip=skip, max_iter=max_iter, dv=dv, strictness=strictness, ...)
+		func_out <- pharmpy$tools$run_ruvsearch(model, results, groups=groups, p_value=p_value, skip=skip, max_iter=max_iter, dv=dv, strictness=strictness, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
@@ -8992,14 +8993,14 @@ run_simulation <- function(model=NULL, ...) {
 #' @description
 #' Run the structsearch tool. For more details, see :ref:`structsearch`.
 #' 
+#' @param model (Model) Pharmpy start model
+#' @param results (ModelfitResults) Results for the start model
 #' @param type (str) Type of model. Currently only 'drug_metabolite' and 'pkpd'
-#' @param model (Model) Search space to test
-#' @param results (ModelfitResults) Initial estimate for the baseline for pkpd models.
-#' @param search_space (str or ModelFeatures (optional)) Initial estimate for E_MAX (for pkpd models only).
-#' @param b_init (numeric (optional)) Initial estimate for EC_50 (for pkpd models only).
-#' @param emax_init (numeric (optional)) Initial estimate for MET (for pkpd models only).
-#' @param ec50_init (numeric (optional)) Results for the start model
-#' @param met_init (numeric (optional)) Pharmpy start model
+#' @param search_space (str or ModelFeatures (optional)) Search space to test
+#' @param b_init (numeric (optional)) Initial estimate for the baseline for pkpd models.
+#' @param emax_init (numeric (optional)) Initial estimate for E_MAX (for pkpd models only).
+#' @param ec50_init (numeric (optional)) Initial estimate for EC_50 (for pkpd models only).
+#' @param met_init (numeric (optional)) Initial estimate for MET (for pkpd models only).
 #' @param extra_model (Model (optional)) Optional extra Pharmpy model to use in TMDD structsearch
 #' @param strictness (str (optional)) Results for the extra model
 #' @param extra_model_results (ModelfitResults (optional)) Strictness criteria
@@ -9012,14 +9013,14 @@ run_simulation <- function(model=NULL, ...) {
 #' \dontrun{
 #' model <- load_example_model("pheno")
 #' results <- load_example_modelfit_results("pheno")
-#' run_structsearch(model_type='pkpd', results=results, model=model)
+#' run_structsearch(model=model, results=results, model_type='pkpd')
 #' }
 #' 
 #' @export
-run_structsearch <- function(type, model, results, search_space=NULL, b_init=NULL, emax_init=NULL, ec50_init=NULL, met_init=NULL, extra_model=NULL, strictness='minimization_successful or (rounding_errors and sigdigs >= 0.1)', extra_model_results=NULL, dv_types=NULL, ...) {
+run_structsearch <- function(model, results, type, search_space=NULL, b_init=NULL, emax_init=NULL, ec50_init=NULL, met_init=NULL, extra_model=NULL, strictness='minimization_successful or (rounding_errors and sigdigs >= 0.1)', extra_model_results=NULL, dv_types=NULL, ...) {
 	tryCatch(
 	{
-		func_out <- pharmpy$tools$run_structsearch(type, model, results, search_space=search_space, b_init=b_init, emax_init=emax_init, ec50_init=ec50_init, met_init=met_init, extra_model=extra_model, strictness=strictness, extra_model_results=extra_model_results, dv_types=dv_types, ...)
+		func_out <- pharmpy$tools$run_structsearch(model, results, type, search_space=search_space, b_init=b_init, emax_init=emax_init, ec50_init=ec50_init, met_init=met_init, extra_model=extra_model, strictness=strictness, extra_model_results=extra_model_results, dv_types=dv_types, ...)
 		if ('pharmpy.workflows.results.Results' %in% class(func_out)) {
 			func_out <- reset_indices_results(func_out)
 		}
diff --git a/man/add_individual_parameter.Rd b/man/add_individual_parameter.Rd
index 5fae80b..2928b4b 100644
--- a/man/add_individual_parameter.Rd
+++ b/man/add_individual_parameter.Rd
@@ -4,12 +4,16 @@
 \alias{add_individual_parameter}
 \title{add_individual_parameter}
 \usage{
-add_individual_parameter(model, name)
+add_individual_parameter(model, name, init = 0.1, lower = 0)
 }
 \arguments{
 \item{model}{(Model) Pharmpy model}
 
 \item{name}{(str) Name of individual/pk parameter}
+
+\item{init}{(numeric) Initial estimate of the population parameter}
+
+\item{lower}{(numeric) Lower bound for the population parameter}
 }
 \value{
 (Model) Pharmpy model object
diff --git a/man/add_peripheral_compartment.Rd b/man/add_peripheral_compartment.Rd
index 74a19ed..74af21b 100644
--- a/man/add_peripheral_compartment.Rd
+++ b/man/add_peripheral_compartment.Rd
@@ -9,7 +9,7 @@ add_peripheral_compartment(model, name = NULL)
 \arguments{
 \item{model}{(Model) Pharmpy model}
 
-\item{name}{(str) Name of compartment to add peripheral to.}
+\item{name}{(str (optional)) Name of compartment to add peripheral to.}
 }
 \value{
 (Model) Pharmpy model object
diff --git a/man/calculate_individual_parameter_statistics.Rd b/man/calculate_individual_parameter_statistics.Rd
index b746eaa..d8136ed 100644
--- a/man/calculate_individual_parameter_statistics.Rd
+++ b/man/calculate_individual_parameter_statistics.Rd
@@ -9,7 +9,7 @@ calculate_individual_parameter_statistics(
   expr_or_exprs,
   parameter_estimates,
   covariance_matrix = NULL,
-  seed = NULL
+  seed = 1234
 )
 }
 \arguments{
@@ -17,13 +17,13 @@ calculate_individual_parameter_statistics(
 
 \item{expr_or_exprs}{(array(BooleanExpr) or array(Expr) or array(str) or BooleanExpr or Expr or str) Parameter estimates}
 
-\item{parameter_estimates}{(array) Parameter uncertainty covariance matrix}
+\item{parameter_estimates}{() Parameter uncertainty covariance matrix}
 
 \item{covariance_matrix}{(data.frame (optional)) expression or iterable of str or expressions
 Expressions or equations for parameters of interest. If equations are used
 the names of the left hand sides will be used as the names of the parameters.}
 
-\item{seed}{(numeric (optional)) Random number generator or integer seed}
+\item{seed}{(numeric) Random number generator or integer seed}
 }
 \value{
 (data.frame) A DataFrame of statistics indexed on parameter and covariate value.
diff --git a/man/calculate_pk_parameters_statistics.Rd b/man/calculate_pk_parameters_statistics.Rd
index 3d20137..123f75c 100644
--- a/man/calculate_pk_parameters_statistics.Rd
+++ b/man/calculate_pk_parameters_statistics.Rd
@@ -8,7 +8,7 @@ calculate_pk_parameters_statistics(
   model,
   parameter_estimates,
   covariance_matrix = NULL,
-  seed = NULL
+  seed = 1234
 )
 }
 \arguments{
@@ -18,7 +18,7 @@ calculate_pk_parameters_statistics(
 
 \item{covariance_matrix}{(data.frame (optional)) Parameter uncertainty covariance matrix}
 
-\item{seed}{(numeric (optional)) Random number generator or seed}
+\item{seed}{(numeric) Random number generator or seed}
 }
 \value{
 (data.frame) A DataFrame of statistics indexed on parameter and covariate value.
diff --git a/man/create_rng.Rd b/man/create_rng.Rd
index 129307a..5bb56ce 100644
--- a/man/create_rng.Rd
+++ b/man/create_rng.Rd
@@ -4,10 +4,10 @@
 \alias{create_rng}
 \title{create_rng}
 \usage{
-create_rng(seed = NULL)
+create_rng(seed = 1234)
 }
 \arguments{
-\item{seed}{(numeric (optional)) Seed for the random number generator or NULL (default) for a randomized seed. If seed
+\item{seed}{(numeric) Seed for the random number generator or NULL (default) for a randomized seed. If seed
 is generator it will be passed through.}
 }
 \value{
diff --git a/man/get_zero_order_inputs.Rd b/man/get_zero_order_inputs.Rd
index 0b661f4..f11e2cc 100644
--- a/man/get_zero_order_inputs.Rd
+++ b/man/get_zero_order_inputs.Rd
@@ -10,7 +10,7 @@ get_zero_order_inputs(model)
 \item{model}{(Model) Pharmpy model}
 }
 \value{
-(sympy.Matrix) Vector of inputs
+(Matrix) Vector of inputs
 }
 \description{
 Get zero order inputs for all compartments
diff --git a/man/has_additive_error_model.Rd b/man/has_additive_error_model.Rd
index 41a54e6..33c8d6b 100644
--- a/man/has_additive_error_model.Rd
+++ b/man/has_additive_error_model.Rd
@@ -9,7 +9,7 @@ has_additive_error_model(model, dv = NULL)
 \arguments{
 \item{model}{(Model) The model to check}
 
-\item{dv}{(str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
+\item{dv}{(Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
 }
 \value{
 (logical) TRUE if the model has an additive error model and FALSE otherwise
diff --git a/man/has_combined_error_model.Rd b/man/has_combined_error_model.Rd
index 8e40fc0..076c895 100644
--- a/man/has_combined_error_model.Rd
+++ b/man/has_combined_error_model.Rd
@@ -9,7 +9,7 @@ has_combined_error_model(model, dv = NULL)
 \arguments{
 \item{model}{(Model) The model to check}
 
-\item{dv}{(str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
+\item{dv}{(Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
 }
 \value{
 (logical) TRUE if the model has a combined error model and FALSE otherwise
diff --git a/man/has_proportional_error_model.Rd b/man/has_proportional_error_model.Rd
index a2e7366..45bd0cf 100644
--- a/man/has_proportional_error_model.Rd
+++ b/man/has_proportional_error_model.Rd
@@ -9,7 +9,7 @@ has_proportional_error_model(model, dv = NULL)
 \arguments{
 \item{model}{(Model) The model to check}
 
-\item{dv}{(str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
+\item{dv}{(Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
 }
 \value{
 (logical) TRUE if the model has a proportional error model and FALSE otherwise
diff --git a/man/remove_peripheral_compartment.Rd b/man/remove_peripheral_compartment.Rd
index 1c08fbe..f08ccdc 100644
--- a/man/remove_peripheral_compartment.Rd
+++ b/man/remove_peripheral_compartment.Rd
@@ -9,7 +9,7 @@ remove_peripheral_compartment(model, name = NULL)
 \arguments{
 \item{model}{(Model) Pharmpy model}
 
-\item{name}{(str) Name of compartment to remove peripheral compartment from.}
+\item{name}{(str (optional)) Name of compartment to remove peripheral compartment from.}
 }
 \value{
 (Model) Pharmpy model object
diff --git a/man/run_allometry.Rd b/man/run_allometry.Rd
index 199df43..80b6b28 100644
--- a/man/run_allometry.Rd
+++ b/man/run_allometry.Rd
@@ -5,8 +5,8 @@
 \title{run_allometry}
 \usage{
 run_allometry(
-  model = NULL,
-  results = NULL,
+  model,
+  results,
   allometric_variable = "WT",
   reference_value = 70,
   parameters = NULL,
@@ -18,9 +18,9 @@ run_allometry(
 )
 }
 \arguments{
-\item{model}{(Model (optional)) Pharmpy model}
+\item{model}{(Model) Pharmpy model}
 
-\item{results}{(ModelfitResults (optional)) Results for model}
+\item{results}{(ModelfitResults) Results for model}
 
 \item{allometric_variable}{(str or Expr) Name of the variable to use for allometric scaling (default is WT)}
 
diff --git a/man/run_amd.Rd b/man/run_amd.Rd
index 2b37eb0..5534e7c 100644
--- a/man/run_amd.Rd
+++ b/man/run_amd.Rd
@@ -28,14 +28,14 @@ run_amd(
   dv_types = NULL,
   mechanistic_covariates = NULL,
   retries_strategy = "all_final",
-  seed = NULL,
+  seed = 1234,
   parameter_uncertainty_method = NULL,
   ignore_datainfo_fallback = FALSE,
   .E = NULL
 )
 }
 \arguments{
-\item{input}{(Model or str or data.frame) Starting model or dataset}
+\item{input}{(Model or str) Starting model or dataset}
 
 \item{results}{(ModelfitResults (optional)) Reults of input if input is a model}
 
@@ -74,7 +74,7 @@ Please use ALLOMETRY in the mfl instead.}
 
 \item{resume}{(logical) Whether to allow resuming previous run}
 
-\item{strictness}{(str (optional)) Strictness criteria}
+\item{strictness}{(str) Strictness criteria}
 
 \item{dv_types}{(list(str=numeric) (optional)) Dictionary of DV types for TMDD models with multiple DVs.}
 
@@ -85,7 +85,7 @@ The effects are extracted from the search space for covsearch.}
 \item{retries_strategy}{(str) Whether or not to run retries tool. Valid options are 'skip', 'all_final' or 'final'.
 Default is 'final'.}
 
-\item{seed}{(numeric (optional)) Random number generator or seed to be used.}
+\item{seed}{(numeric) Random seed to be used.}
 
 \item{parameter_uncertainty_method}{(str (optional)) Parameter uncertainty method.}
 
diff --git a/man/run_covsearch.Rd b/man/run_covsearch.Rd
index 672d9cb..5dcf268 100644
--- a/man/run_covsearch.Rd
+++ b/man/run_covsearch.Rd
@@ -5,25 +5,30 @@
 \title{run_covsearch}
 \usage{
 run_covsearch(
+  model,
+  results,
   search_space,
   p_forward = 0.01,
   p_backward = 0.001,
   max_steps = -1,
   algorithm = "scm-forward-then-backward",
-  results = NULL,
-  model = NULL,
   max_eval = FALSE,
   adaptive_scope_reduction = FALSE,
   strictness = "minimization_successful or (rounding_errors and sigdigs>=0.1)",
   naming_index_offset = 0,
   nsamples = 10,
-  .statsmodels = FALSE,
-  .weighted_linreg = FALSE,
-  .lin_filter = 0,
+  .samba_max_covariates = 3,
+  .samba_selection_criterion = "bic",
+  .samba_linreg_method = "ols",
+  .samba_stepwise_lcs = NULL,
   ...
 )
 }
 \arguments{
+\item{model}{(Model) Pharmpy model}
+
+\item{results}{(ModelfitResults) Results of model}
+
 \item{search_space}{(str or ModelFeatures) MFL of covariate effects to try}
 
 \item{p_forward}{(numeric) The p-value to use in the likelihood ratio test for forward steps}
@@ -35,10 +40,6 @@ run_covsearch(
 \item{algorithm}{(str) The search algorithm to use. Currently, 'scm-forward' and
 'scm-forward-then-backward' are supported.}
 
-\item{results}{(ModelfitResults (optional)) Results of model}
-
-\item{model}{(Model (optional)) Pharmpy model}
-
 \item{max_eval}{(logical) Limit the number of function evaluations to 3.1 times that of the
 base model. Default is FALSE.}
 
@@ -47,22 +48,23 @@ after all significant effects have been tested. Once all these have been
 tested, try adding the stashed effects once more with a regular forward approach.
 Default is FALSE}
 
-\item{strictness}{(str (optional)) Strictness criteria}
+\item{strictness}{(str) Strictness criteria}
 
 \item{naming_index_offset}{(numeric (optional)) index offset for naming of runs. Default is 0.}
 
 \item{nsamples}{(numeric) Number of samples from individual parameter conditional distribution for linear covariate model selection.
-\code{nsamples=0} uses ETAs to for linear model selection, whereas \code{nsample}>=1 generates MCMC samples with an
-additional SAEM estimation step. When multiple samples are generated, linear mixed effects model will be
-used to fit the linear models.
 Default is 10, i.e. generating 10 samples per subject}
 
-\item{.statsmodels}{(logical) Estimation tool for SAMBA linear covariate model fitting. 'TRUE' calls statsmodel's
-functionalities, whereas 'FALSE' calls nonmem.}
+\item{.samba_max_covariates}{(numeric (optional)) Maximum number of covariate inclusion allowed in linear covariate screening for each parameter.}
+
+\item{.samba_selection_criterion}{(str) Method used to fit linear covariate models. Currently, Ordinary Least Squares (ols),
+Weighted Least Squares (wls), and Linear Mixed-Effects (lme) are supported.}
 
-\item{.weighted_linreg}{(logical) When using nonmem to run linear covariate models, 'TRUE' uses ETC as weight to run WLS.}
+\item{.samba_linreg_method}{(str) Method used for linear and nonlinear model selection in SAMBA methods. Currently, BIC and LRT are
+supported.}
 
-\item{.lin_filter}{(numeric) Option to control the number of covariates passed to nonlinear selection}
+\item{.samba_stepwise_lcs}{(logical (optional)) Use stepwise linear covariate screening or not. By default, SAMBA methods use stepwise LCS whereas SCM-LCS uses
+non-stepwise LCS}
 
 \item{...}{Arguments to pass to tool}
 }
@@ -77,7 +79,7 @@ Run COVsearch tool. For more details, see :ref:\code{covsearch}.
 model <- load_example_model("pheno")
 results <- load_example_modelfit_results("pheno")
 search_space <- 'COVARIATE(c(CL, V), c(AGE, WT), EXP)'
-res <- run_covsearch(search_space, model=model, results=results)
+res <- run_covsearch(model=model, results=results, search_space=search_space)
 }
 
 }
diff --git a/man/run_iivsearch.Rd b/man/run_iivsearch.Rd
index 56ffaf7..6ce0c13 100644
--- a/man/run_iivsearch.Rd
+++ b/man/run_iivsearch.Rd
@@ -5,13 +5,13 @@
 \title{run_iivsearch}
 \usage{
 run_iivsearch(
+  model,
+  results,
   algorithm = "top_down_exhaustive",
   iiv_strategy = "no_add",
   rank_type = "bic",
   linearize = FALSE,
   cutoff = NULL,
-  results = NULL,
-  model = NULL,
   keep = c("CL"),
   strictness = "minimization_successful or (rounding_errors and sigdigs>=0.1)",
   correlation_algorithm = NULL,
@@ -21,6 +21,10 @@ run_iivsearch(
 )
 }
 \arguments{
+\item{model}{(Model) Pharmpy model}
+
+\item{results}{(ModelfitResults) Results for model}
+
 \item{algorithm}{(str) Which algorithm to run when determining number of IIVs.}
 
 \item{iiv_strategy}{(str) If/how IIV should be added to start model. Default is 'no_add'.}
@@ -32,13 +36,9 @@ run_iivsearch(
 \item{cutoff}{(numeric (optional)) Cutoff for which value of the ranking function that is considered significant. Default
 is NULL (all models will be ranked)}
 
-\item{results}{(ModelfitResults (optional)) Results for model}
-
-\item{model}{(Model (optional)) Pharmpy model}
-
 \item{keep}{(array(str) (optional)) List of IIVs to keep. Default is "CL"}
 
-\item{strictness}{(str (optional)) Strictness criteria}
+\item{strictness}{(str) Strictness criteria}
 
 \item{correlation_algorithm}{(str (optional)) Which algorithm to run for the determining block structure of added IIVs. If NULL, the
 algorithm is determined based on the 'algorithm' argument}
@@ -61,7 +61,7 @@ Run IIVsearch tool. For more details, see :ref:\code{iivsearch}.
 \dontrun{
 model <- load_example_model("pheno")
 results <- load_example_modelfit_results("pheno")
-run_iivsearch('td_brute_force', results=results, model=model)
+run_iivsearch(model=model, results=results, algorithm='td_brute_force')
 }
 
 }
diff --git a/man/run_iovsearch.Rd b/man/run_iovsearch.Rd
index 931eb07..c10c75d 100644
--- a/man/run_iovsearch.Rd
+++ b/man/run_iovsearch.Rd
@@ -5,19 +5,23 @@
 \title{run_iovsearch}
 \usage{
 run_iovsearch(
+  model,
+  results,
   column = "OCC",
   list_of_parameters = NULL,
   rank_type = "bic",
   cutoff = NULL,
   distribution = "same-as-iiv",
-  results = NULL,
-  model = NULL,
   strictness = "minimization_successful or (rounding_errors and sigdigs>=0.1)",
   E = NULL,
   ...
 )
 }
 \arguments{
+\item{model}{(Model) Pharmpy model}
+
+\item{results}{(ModelfitResults) Results for model}
+
 \item{column}{(str) Name of column in dataset to use as occasion column (default is 'OCC')}
 
 \item{list_of_parameters}{(array(str or array(str)) (optional)) List of parameters to test IOV on, if none all parameters with IIV will be tested (default)}
@@ -29,10 +33,6 @@ is NULL (all models will be ranked)}
 
 \item{distribution}{(str) Which distribution added IOVs should have (default is same-as-iiv)}
 
-\item{results}{(ModelfitResults (optional)) Results for model}
-
-\item{model}{(Model (optional)) Pharmpy model}
-
 \item{strictness}{(str (optional)) Strictness criteria}
 
 \item{E}{(numeric or str (optional)) Expected number of predictors (used for mBIC). Must be set when using mBI}
@@ -49,7 +49,7 @@ Run IOVsearch tool. For more details, see :ref:\code{iovsearch}.
 \dontrun{
 model <- load_example_model("pheno")
 results <- load_example_modelfit_results("pheno")
-run_iovsearch('OCC', results=results, model=model)
+run_iovsearch(model=model, results=results, column='OCC')
 }
 
 }
diff --git a/man/run_modelsearch.Rd b/man/run_modelsearch.Rd
index 4c47ebb..a4131b4 100644
--- a/man/run_modelsearch.Rd
+++ b/man/run_modelsearch.Rd
@@ -5,19 +5,23 @@
 \title{run_modelsearch}
 \usage{
 run_modelsearch(
+  model,
+  results,
   search_space,
   algorithm = "reduced_stepwise",
   iiv_strategy = "absorption_delay",
   rank_type = "bic",
   cutoff = NULL,
-  results = NULL,
-  model = NULL,
   strictness = "minimization_successful or (rounding_errors and sigdigs >= 0.1)",
   E = NULL,
   ...
 )
 }
 \arguments{
+\item{model}{(Model) Pharmpy model}
+
+\item{results}{(ModelfitResults) Results for model}
+
 \item{search_space}{(str or ModelFeatures) Search space to test. Either as a string or a ModelFeatures object.}
 
 \item{algorithm}{(str) Algorithm to use.}
@@ -29,11 +33,7 @@ run_modelsearch(
 \item{cutoff}{(numeric (optional)) Cutoff for which value of the ranking function that is considered significant. Default
 is NULL (all models will be ranked)}
 
-\item{results}{(ModelfitResults (optional)) Results for model}
-
-\item{model}{(Model (optional)) Pharmpy model}
-
-\item{strictness}{(str (optional)) Strictness criteria}
+\item{strictness}{(str) Strictness criteria}
 
 \item{E}{(numeric or str (optional)) Expected number of predictors (used for mBIC). Must be set when using mBI}
 
@@ -48,8 +48,9 @@ Run Modelsearch tool. For more details, see :ref:\code{modelsearch}.
 \examples{
 \dontrun{
 model <- load_example_model("pheno")
-results <- load_example_modelfit_results("pheno")
-run_modelsearch('ABSORPTION(ZO);PERIPHERALS(1)', 'exhaustive', results=results, model=model)
+res <- load_example_modelfit_results("pheno")
+search_space <- 'ABSORPTION(ZO);PERIPHERALS(1)'
+run_modelsearch(model=model, results=res, search_space=search_space, algorithm='exhaustive')
 }
 
 }
diff --git a/man/run_retries.Rd b/man/run_retries.Rd
index a8e216a..de259dc 100644
--- a/man/run_retries.Rd
+++ b/man/run_retries.Rd
@@ -13,7 +13,6 @@ run_retries(
   strictness = "minimization_successful or (rounding_errors and sigdigs >= 0.1)",
   scale = "UCP",
   prefix_name = "",
-  seed = NULL,
   ...
 )
 }
@@ -28,13 +27,11 @@ run_retries(
 
 \item{use_initial_estimates}{(logical) Use initial parameter estimates instead of final estimates of input model when creating candidate models.}
 
-\item{strictness}{(str (optional)) Strictness criteria. The default is "minimization_successful or (rounding_errors and sigdigs >= 0.1)".}
+\item{strictness}{(str) Strictness criteria. The default is "minimization_successful or (rounding_errors and sigdigs >= 0.1)".}
 
 \item{scale}{(str (optional)) Which scale to update the initial values on. Either normal scale or UCP scale.}
 
-\item{prefix_name}{(str (optional)) Prefix the candidate model names with given string.}
-
-\item{seed}{(numeric (optional)) Random number generator or seed to be used}
+\item{prefix_name}{(str (optional)) Prefix the candidate model names with given string}
 
 \item{...}{Arguments to pass to tool}
 }
diff --git a/man/run_ruvsearch.Rd b/man/run_ruvsearch.Rd
index d27a48e..11b4fea 100644
--- a/man/run_ruvsearch.Rd
+++ b/man/run_ruvsearch.Rd
@@ -5,8 +5,8 @@
 \title{run_ruvsearch}
 \usage{
 run_ruvsearch(
-  model = NULL,
-  results = NULL,
+  model,
+  results,
   groups = 4,
   p_value = 0.001,
   skip = NULL,
@@ -17,9 +17,9 @@ run_ruvsearch(
 )
 }
 \arguments{
-\item{model}{(Model (optional)) Pharmpy model}
+\item{model}{(Model) Pharmpy model}
 
-\item{results}{(ModelfitResults (optional)) Results of model}
+\item{results}{(ModelfitResults) Results of model}
 
 \item{groups}{(numeric) The number of bins to use for the time varying models}
 
@@ -31,7 +31,7 @@ run_ruvsearch(
 
 \item{dv}{(numeric (optional)) Which DV to assess the error model for.}
 
-\item{strictness}{(str (optional)) Strictness criteri}
+\item{strictness}{(str) Strictness criteri}
 
 \item{...}{Arguments to pass to tool}
 }
diff --git a/man/run_structsearch.Rd b/man/run_structsearch.Rd
index 9c18524..d3b558c 100644
--- a/man/run_structsearch.Rd
+++ b/man/run_structsearch.Rd
@@ -5,9 +5,9 @@
 \title{run_structsearch}
 \usage{
 run_structsearch(
-  type,
   model,
   results,
+  type,
   search_space = NULL,
   b_init = NULL,
   emax_init = NULL,
@@ -21,21 +21,21 @@ run_structsearch(
 )
 }
 \arguments{
-\item{type}{(str) Type of model. Currently only 'drug_metabolite' and 'pkpd'}
+\item{model}{(Model) Pharmpy start model}
 
-\item{model}{(Model) Search space to test}
+\item{results}{(ModelfitResults) Results for the start model}
 
-\item{results}{(ModelfitResults) Initial estimate for the baseline for pkpd models.}
+\item{type}{(str) Type of model. Currently only 'drug_metabolite' and 'pkpd'}
 
-\item{search_space}{(str or ModelFeatures (optional)) Initial estimate for E_MAX (for pkpd models only).}
+\item{search_space}{(str or ModelFeatures (optional)) Search space to test}
 
-\item{b_init}{(numeric (optional)) Initial estimate for EC_50 (for pkpd models only).}
+\item{b_init}{(numeric (optional)) Initial estimate for the baseline for pkpd models.}
 
-\item{emax_init}{(numeric (optional)) Initial estimate for MET (for pkpd models only).}
+\item{emax_init}{(numeric (optional)) Initial estimate for E_MAX (for pkpd models only).}
 
-\item{ec50_init}{(numeric (optional)) Results for the start model}
+\item{ec50_init}{(numeric (optional)) Initial estimate for EC_50 (for pkpd models only).}
 
-\item{met_init}{(numeric (optional)) Pharmpy start model}
+\item{met_init}{(numeric (optional)) Initial estimate for MET (for pkpd models only).}
 
 \item{extra_model}{(Model (optional)) Optional extra Pharmpy model to use in TMDD structsearch}
 
@@ -57,7 +57,7 @@ Run the structsearch tool. For more details, see :ref:\code{structsearch}.
 \dontrun{
 model <- load_example_model("pheno")
 results <- load_example_modelfit_results("pheno")
-run_structsearch(model_type='pkpd', results=results, model=model)
+run_structsearch(model=model, results=results, model_type='pkpd')
 }
 
 }
diff --git a/man/sample_individual_estimates.Rd b/man/sample_individual_estimates.Rd
index d13da09..cab3de0 100644
--- a/man/sample_individual_estimates.Rd
+++ b/man/sample_individual_estimates.Rd
@@ -10,7 +10,7 @@ sample_individual_estimates(
   individual_estimates_covariance,
   parameters = NULL,
   samples_per_id = 100,
-  seed = NULL
+  seed = 1234
 )
 }
 \arguments{
@@ -24,7 +24,7 @@ sample_individual_estimates(
 
 \item{samples_per_id}{(numeric) Number of samples per individual}
 
-\item{seed}{(numeric (optional)) Random number generator or seed}
+\item{seed}{(numeric) Random number generator or seed}
 }
 \value{
 (data.frame) Pool of samples in a DataFrame
diff --git a/man/sample_parameters_from_covariance_matrix.Rd b/man/sample_parameters_from_covariance_matrix.Rd
index 606ded3..bec1232 100644
--- a/man/sample_parameters_from_covariance_matrix.Rd
+++ b/man/sample_parameters_from_covariance_matrix.Rd
@@ -11,7 +11,7 @@ sample_parameters_from_covariance_matrix(
   force_posdef_samples = NULL,
   force_posdef_covmatrix = FALSE,
   n = 1,
-  seed = NULL
+  seed = 1234
 )
 }
 \arguments{
@@ -28,7 +28,7 @@ default and means never and 0 means always}
 
 \item{n}{(numeric) Number of samples}
 
-\item{seed}{(numeric (optional)) Random number generator}
+\item{seed}{(numeric) Random number generator}
 }
 \value{
 (data.frame) A dataframe with one sample per row
diff --git a/man/sample_parameters_uniformly.Rd b/man/sample_parameters_uniformly.Rd
index c34d59a..3a91a0a 100644
--- a/man/sample_parameters_uniformly.Rd
+++ b/man/sample_parameters_uniformly.Rd
@@ -10,7 +10,7 @@ sample_parameters_uniformly(
   fraction = 0.1,
   force_posdef_samples = NULL,
   n = 1,
-  seed = NULL,
+  seed = 1234,
   scale = "normal"
 )
 }
@@ -26,7 +26,7 @@ positive definite covariance matrices.}
 
 \item{n}{(numeric) Number of samples}
 
-\item{seed}{(numeric (optional)) Random number generator or seed}
+\item{seed}{(numeric) Random number generator or seed}
 
 \item{scale}{(str) Scale to perform sampling on. Valid options are 'normal' and 'UCP'}
 }
diff --git a/man/set_additive_error_model.Rd b/man/set_additive_error_model.Rd
index 2981f21..00d2ff2 100644
--- a/man/set_additive_error_model.Rd
+++ b/man/set_additive_error_model.Rd
@@ -9,7 +9,7 @@ set_additive_error_model(model, dv = NULL, data_trans = NULL, series_terms = 2)
 \arguments{
 \item{model}{(Model) Set error model for this model}
 
-\item{dv}{(str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
+\item{dv}{(Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
 
 \item{data_trans}{(numeric or str or Expr (optional)) A data transformation expression or NULL (default) to use the transformation
 specified by the model. Series expansion will be used for approximation.}
diff --git a/man/set_combined_error_model.Rd b/man/set_combined_error_model.Rd
index ae8fa85..8eb8db1 100644
--- a/man/set_combined_error_model.Rd
+++ b/man/set_combined_error_model.Rd
@@ -9,7 +9,7 @@ set_combined_error_model(model, dv = NULL, data_trans = NULL)
 \arguments{
 \item{model}{(Model) Set error model for this model}
 
-\item{dv}{(str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
+\item{dv}{(Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
 
 \item{data_trans}{(numeric or str or Expr (optional)) A data transformation expression or NULL (default) to use the transformation
 specified by the model.}
diff --git a/man/set_peripheral_compartments.Rd b/man/set_peripheral_compartments.Rd
index 8a7db8d..17678fc 100644
--- a/man/set_peripheral_compartments.Rd
+++ b/man/set_peripheral_compartments.Rd
@@ -11,7 +11,7 @@ set_peripheral_compartments(model, n, name = NULL)
 
 \item{n}{(numeric) Number of transit compartments}
 
-\item{name}{(str) Name of compartment to add peripheral to.}
+\item{name}{(str (optional)) Name of compartment to add peripheral to.}
 }
 \value{
 (Model) Pharmpy model object
diff --git a/man/set_proportional_error_model.Rd b/man/set_proportional_error_model.Rd
index 6865494..a92865a 100644
--- a/man/set_proportional_error_model.Rd
+++ b/man/set_proportional_error_model.Rd
@@ -14,7 +14,7 @@ set_proportional_error_model(
 \arguments{
 \item{model}{(Model) Set error model for this model}
 
-\item{dv}{(str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
+\item{dv}{(Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
 
 \item{data_trans}{(numeric or str or Expr (optional)) A data transformation expression or NULL (default) to use the transformation
 specified by the model.}
diff --git a/man/set_simulation.Rd b/man/set_simulation.Rd
index 5d1f133..953fd1f 100644
--- a/man/set_simulation.Rd
+++ b/man/set_simulation.Rd
@@ -4,7 +4,7 @@
 \alias{set_simulation}
 \title{set_simulation}
 \usage{
-set_simulation(model, n = 1, seed = 64206)
+set_simulation(model, n = 1, seed = 1234)
 }
 \arguments{
 \item{model}{(Model) Pharmpy model}
diff --git a/man/set_time_varying_error_model.Rd b/man/set_time_varying_error_model.Rd
index 695944f..2682f4b 100644
--- a/man/set_time_varying_error_model.Rd
+++ b/man/set_time_varying_error_model.Rd
@@ -13,7 +13,7 @@ set_time_varying_error_model(model, cutoff, idv = "TIME", dv = NULL)
 
 \item{idv}{(str) Time or time after dose, default is Time}
 
-\item{dv}{(str or Expr or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
+\item{dv}{(Expr or str or numeric (optional)) Name or DVID of dependent variable. NULL for the default (first or only)}
 }
 \value{
 (Model) Pharmpy model object